Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

Enhanced (or engineered) geothermal systems (EGS) have evolved from the hot dry rock concept, implemented for the first time at Fenton Hill in 1977. This paper systematically reviews all of the EGS projects worldwide, based on the information available in the public domain. The projects are classified by country, reservoir type, depth, reservoir temperature, stimulation methods, associated seismicity, plant capacity and current status. Thirty five years on from the first EGS implementation, the geothermal community can benefit from the lessons learnt and take a more objective approach to the pros and cons of ‘conventional’ EGS systems.

/pdf/a-systematic-review-of-enhanced-or-engineered-geothermal-3ab4b4edio.pdf

A systematic review of enhanced (or engineered) geothermal systems: past, present and future

Global review of human-induced earthquakes

Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.

/pdf/global-quieting-of-high-frequency-seismic-noise-due-to-covid-443vac1t5f.pdf

Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures.

Visual testing, as one of the oldest methods for nondestructive testing (NDT), plays a large role in the inspection of civil infrastructure. As NDT has evolved, more quantitative techniques...

Video Camera–Based Vibration Measurement for Civil Infrastructure Applications

METSEIS: An experiment to quantify meteorological and seismological interactions

Railway switches are a crucial asset since they enable trains to change tracks without stopping. Switch failures can compromise a larger part of the railway infrastructure, which can have a negative impact on reputation and revenues. Switches are a costly asset due to frequent inspections, maintenance and renewal of components. Therefore knowing current and future asset condition can be helpful in optimizing switch maintenance to prevent complete failure. The goal of the research presented here is to exploit switch condition monitoring and weather data to identify switch failures on an early stage. Approaches for detection of anomalous switch behavior and prediction of failures are developed. To validate the anomaly detection results obtained by applying the Isolation Forest algorithm, two different annotated data sets are considered. It is found that the anomaly detection approach performs well when applied to a switch, which is characterized by narrow feature distributions within temperature bins. Moreover first results from an Autoregressive Integrated Moving Average model for failure evolution prediction are presented.

Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Railway Switch Failures

Fault Plane Solutions of Microseismic Events near Landau and Insheim, SW Germany

Bucharest, the capital of Romania with about 2.5 million inhabitants, is frequently struck by intense, damaging earthquakes (1940, 1977, 1986 & 1990). Within the framework of the Collaborative Research Centre 461 (CRC 461) "Strong Earthquakes A Challenge for Geosciences and Civil Engineering" and the “Romanian Group for Strong Earthquakes” seismic wavefields were recorded continuously in Bucharest with broadband instruments for 9 months. During this URban Seismology (URS) project the KArlsruhe BroadBand Array (KABBA) with 32 mobile broadband stations was installed in the city centre and the periphery of Bucharest between October 2003 and August 2004. The aims of the field project are on one hand the recording of local, regional and teleseismic earthquakes and on the other hand the continuous acquisition of urban seismic noise. These data should serve as basis for comprehensive studies of the subsurface of Bucharest and the properties of the seismic wavefield in a major city. The analysis of recordings from regional intermediate-depth Vrancea earthquakes provides information on the properties of the related seismic wavefields, amplitude variations across the network, crustal structure from receiver functions and transfer functions of a ten-story tower building in Magurele. Teleseismic waves were used to study low-frequency amplitude variations as well as lithospheric structure from receiver functions and Love wave dispersion. Ambient seismic noise is analysed for site effects using the horizontal-over-vertical spectral ratio as well as to characterise the noise sources and their temporal behaviour. Here we summarise the main results, for further details we refer to the reference list.

https://digbib.ubka.uni-karlsruhe.de/volltexte/beilagen/1/proceedings/pdf/42_Symposium_Bucharest_07_037_Balan.pdf

Results from the URban Seismology (URS) project in Bucharest, Romania

BENJAMIN BAASCH1, MICHAEL ROTH2, SEBASTIAN SCHULZ3 and JÖRN C. GROOS4 1Institute of Transportation Systems, German Aerospace Center (DLR), Rutherfordstr. 2, 12489 Berlin, Germany. E-mail: benjamin.baasch@dlr.de 2Institute of Transportation Systems, German Aerospace Center (DLR), Lilienthalplatz 7, 38108 Braunschweig, Germany. E-mail: m.roth@dlr.de 3Institute of Transportation Systems, German Aerospace Center (DLR), Rutherfordstr. 2, 12489 Berlin, Germany. E-mail: s.schulz@dlr.de 4Institute of Transportation Systems, German Aerospace Center (DLR), Lilienthalplatz 7, 38108 Braunschweig, Germany. E-mail: joern.groos@dlr.de

/pdf/an-unsupervised-machine-learning-approach-to-extract-wheel-a59378d4c6.pdf

Jörn Christoffer Groos

Papers

METSEIS: An experiment to quantify meteorological and seismological interactions

Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Railway Switch Failures

Fault Plane Solutions of Microseismic Events near Landau and Insheim, SW Germany

Results from the URban Seismology (URS) project in Bucharest, Romania

An unsupervised machine learning approach to extract wheel and track health status indicators from train-borne accelerometer data